According to the demand of spread spectrum signal processing in lower SNR conditions, a novel algorithm for blind estimation of the pseudo-noise (PN) sequence of the direct sequence spread spectrum (DSSS) signal is proposed based on the similarities among the DSSS signals and the eigenanalysis technique. Firstly, through segmentation processing and average cross-correlation calculation, the starting symbol of PN sequence is determined. Then, by utilizing the eigenanalysis technique PN sequence can be estimated accurately without any information of the DSSS signal. By theoretical analysis and logical deduction, two conclusions about the estimation efficiency and estimation error between the proposed algorithm and the existing algorithms are presented and proved. Simulation results show that compared with the existing algorithms under the same condition, the proposed algorithm not only overcomes the partial-encode problem, but also improves estimation performance by 2 dB, which indicates that the proposed algorithm achieves an accurate estimation of PN sequence in lower SNR conditions. According to the demand of spread spectrum signal processing in lower SNR conditions, a novel algorithm for blind estimation of the pseudo-noise (PN) sequence of the direct sequence spread spectrum (DSSS) signal is proposed based on the similarities among the DSSS signals and The eigenanalysis technique. Firstly, through segmentation processing and average cross-correlation calculation, the starting symbol of PN sequence is determined. Then, by utilizing the eigenanalysis technique PN sequence can be estimated accurately without any information of the DSSS signal. By theoretical analysis and logical results, two estimates about the estimation efficiency and estimation error between the proposed algorithm and the existing algorithms are presented and proved. Simulation results show that compared with the existing algorithms under the same condition, the proposed algorithm not only overcomes the partial-encode problem , but also improves estimation performance by 2 dB, which indicates that the proposed algorithm achieves an accurate estimation of PN sequence in lower SNR conditions.